Bayesian network inference modeling identifies TRIB1 as a novel regulator of cell-cycle progression and survival in cancer cells

Molecular networks governing cellular responses to targeted therapies are complex dynamic systems with non-intuitive behaviors. Here we applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network modeling, we generated predictions of G1-S transition. The model identified known components of the cell cycle machinery, such as CCND1, CCNE2 and CDC25A, as well as novel regulators of G1-S transition IER2, TRIB1 and TRIM27. Experimental validation of this model confirmed 10 of 12 predicted genes to have a role in progression through the G1-S phase transition of the cell cycle. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NF-κB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NF-κB and its target genes IL-8 and CSF2, and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role for TRIB1 in cell cycle and survival that is mediated via the modulation of NF-κB signaling.